Non-invasive prediction of risk for sudden cardiac death

ABSTRACT

A method and apparatus for the quantitative determination of an individual&#39;s risk for sudden cardiac death (SCD) is described. Risk determination is accomplished and may have a sensitivity and specificity of greater than 95%, by generating linear and nonlinear mathematical digital ECG-constructed models from digital ECG-type data of an individual&#39;s digital ECG, determining stability/instability of digital ECG-constructed control model systems corresponding to the digital ECG-constructed models by a plurality of techniques and transforming stability/instability values obtained by the determining stability/instability into a quantitative value reflecting an individual&#39;s risk for SCD.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.15/759,386, filed on Mar. 12, 2018, which is a national phase entryunder 35 U.S.C. § 371 of International Application No. PCT/US2016/51460,filed Sep. 13, 2016, published in English, which claims priority fromU.S. Provisional Patent Application No. 62/220,462, filed Sep. 18, 2015,all of which are incorporated herein by reference.

FIELD OF THE TECHNOLOGY

The present technology relates to a method for quantitativedetermination of any individual's risk for sudden cardiac death (SCD),which is also often referred to as sudden cardiac arrest (SCA) ormassive heart attack. More specifically, the present technology mayrelate to a noninvasive apparatus, system, device and method, such asusing digitized electrocardiogram (ECG) data from a standard restingECG, to accurately, rapidly, in near-real time, and easily identify in aquantitative manner individuals at risk for sudden cardiac death withhigh sensitivity and high specificity.

BACKGROUND OF THE TECHNOLOGY

Sudden cardiac death (SCD) is defined as the unexpected death due tocardiac causes of persons with or without underlying cardiac disease,with no other known cause for death. SCD occurs within a short period oftime, for example, generally within one hour, following the onset ofsymptoms (if any symptoms are encountered).

SCD is a major public health problem as it has reached epidemicproportions, responsible for at least 325,000 deaths per year in theUnited States alone. (See Goldberger. Circulation. 2008; 118:000-000;Zipes, D. P., et al., ACC/AHA/ESC 2006 Guidelines for Management ofPatients With Ventricular Arrhythmias and the Prevention of SuddenCardiac Death—Executive Summary. Circulation, 2006: p. CIRCULATIONAHA.106.178104). SCD is the second leading cause of death in the U.S.,responsible for slightly less deaths than myocardial infarction. Despitethe decreased incidence of all cardiac deaths secondary to improvedmedical treatment, SCD continues to represent about half of all cardiacdeaths. (See Ezekowitz. Ann Intern Med. August 2007; 21; 147(4):251-62).

Most cases of SCD are related to cardiac ventricular arrhythmias(ventricular tachycardia, or VT). Coronary artery disease is associatedwith the largest number of SCDs. Acute coronary syndrome (ACS) can leadto malignant arrhythmias that are the result of ischemia. Additionally,coronary artery disease (CAD) may lead to microscopic or macroscopicscar formation that can represent substrate for malignant arrhythmias.

Congestive Heart Failure (CHF) with or without CAD is another cardiacillness associated with a significant risk for SCD. In addition,cardiomyopathy, left ventricular hypertrophy (LVH), myocarditis,hypertrophic cardiomyopathy, congenital coronary artery anomalies, andmyxomatous mitral valve disease are associated with an increased SCDrisk. Finally, the presence of channelopathies such as Brugada syndromeand congenital heart disease or acquired long QT syndrome, idiopathicventricular fibrillation (VF), Arrhythmogenic Right VentricularCardiomyopathy (ARVC), catecholaminergic VT, and Wolff-Parkinson-White(WPW) syndrome increase the risk of SCD

Implantable cardioverter-defibrillator (ICD) therapy has significantlydecreased the occurrence of SCD in high-risk patients, but has donelittle to reduce the overall incidence of SCD nationally. (See Bardy. NEngl J Med. 2005 Jan. 20; 352(3):225-37). This is explained by the factthat two-thirds of patients suffering SCD are in low or intermediaterisk groups, resulting in the greatest absolute number of patientdeaths. However, multiple trials have repeatedly demonstrated thatpatients in low and intermediate risk groups do not benefit fromprophylactic ICD placement. This result demonstrates the lack ofsensitivity and specificity of contemporary methods used to stratifyindividuals for SCD risk. The ability to prevent SCD using ICDs is ofgreat importance, and it demonstrates the critical need for a highlysensitive and highly specific method for identifying individuals at riskfor SCD.

Based on the foregoing, the identification of individuals presentlyconsidered to have no, low, or intermediate risk for SCD as defined bypresently available risk-stratification methods—but in reality are athigh risk—continues to be a major public health concern.

Presently available techniques for the identification of individuals atrisk for SCD include medical history, (e.g., history of coronary arterydisease (CAD), congestive heart failure (CHF), decreased leftventricular ejection fraction (LVEF), prior myocardial infarction,structural or ischemic heart disease), Holter monitoring, heart ratevariability analysis, signal averaged electrocardiography (SAECG),microvolt T-wave alternans analysis, ambulatory ECG monitoring,metabolic factors and/or parasympathetic tone, heart rate turbulencestudies, baroreceptor sensitivity studies and the presence of myocardialscar as detected using magnetic resonance imaging (MRI). Severelyreduced LVEF and the presence of advanced CHF (NYHA Class III or IV)currently serve as the main identifiers for patients at high-risk forSCD and, therefore, for identifying who may benefit from ICD therapy.(See Epstein. Heart Rhythm. 2008 June; 5(6):934-55; Chugh. Nat RevCardiol. 2010 Jun. 7 (6): 318-326, available athttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC3052394/). However, nopresently available method, including those listed above, alone or incombination, has clinically acceptable sensitivity or specificity forthe identification of individuals at risk for SCD. A detailed discussionof some exemplary techniques is provided herewith.

Left Ventricular Ejection Fraction AND NYHA Class III or IV CHF

Left ventricular ejection fraction (LVEF), as evaluated by one of manymodalities, and the presence of NHHA Class III or Class IV CHF are thetwo major predictors of SCD. They are presently the two principleindications used to determine which patients are candidates for ICDplacement. (Of course, a history of a prior SCD event is an absoluteindication for ICD placement.) (See Epstein. Heart Rhythm. 2008 June;5(6):934-55; Rouleau. J Am Coll Cardiol 1996; 27:1119-27). Assessment ofejection fraction (EF) has multiple advantages such as accessibility,ease of use, and relative reproducibility and has been the majordeterminant of patients who are considered for prophylactic ICDimplantation. Several trials that have found that an LVEF≤35%,particularly with symptoms of heart failure (CHF, Class III or IV), haveserved as the accepted marker for identifying high-risk patients.(Bardy. N Engl J Med. 2005 Jan. 20; 352(3):225-37). Conversely, multiplestudies have found an LVEF≥40% is not an accurate marker for those atincreased risk for SCD. This finding suggests that EF may be less usefulas a marker for SCD once the EF is greater than 40% on a populationbasis. (See Ikeda. J AmColl Cardiol 2006; 48:2268-74).

Randomized clinical trials concerning prophylactic ICD implantation haveevaluated patients with depressed LVEF. The implications of thesestudies in the treatment of SCD are limited since most cases of SCD donot occur in patients with low LVEF. (See Zipes, D. P., et al.,ACC/AHA/ESC 2006 Guidelines for Management of Patients With VentricularArrhythmias and the Prevention of Sudden Cardiac Death—ExecutiveSummary. Circulation, 2006: p. CIRCULATION AHA.106.178104). Studies suchas multicenter automatic defibrillator implantation trial I (MADIT I),MADIT II and sudden cardiac death in heart failure trial (SCD-HeFT) haveall shown significant reductions in arrhythmic and overall mortalitywith ICD therapy in patients with severely decreased LVEFs. However, themajority of patients who were evaluated, and showed benefit from theprophylactic ICD implantation, had LVEFs≤25%, limiting the ability toextrapolate this data to the low and intermediate risk groups.

Analysis of the multicenter unsustained tachycardia trial (MUSTT)demonstrates that LVEF does not, in fact, represent the greatest risk oftotal and arrhythmic death. New York Heart Association (NYHA) class,history of heart failure, non-sustained VT, enrollment as inpatient, andatrial fibrillation all portended greater risk as individual markers.Patients with LVEF<30% but with no other risk factors may have a lowerpredicted mortality risk than patients with LVEF>30% as well as otherrisk factors. Risk of SCD in patients with cardiomyopathy depends onmultiple variables in addition to LVEF and may be further elucidatedusing other methods. (See Salerno-Uriarte. J Am Coll Cardiol 2007;50:1896-904). Overall, EF<35% and/or the presence of Class III or ClassIV CHF are important criteria (although present in only a minority ofSCD patients) for ICD placement. However, although they are the bestpresently available methods for SCD risk prediction, they—alone or incombination with other methods—suffer from unacceptably low sensitivityand specificity.

Signal-Averaged Electrocardiogram

In patients with VT, areas of scar may result in slow conduction andprolonged activation of segmental regions of the ventricle. This slowingmay manifest itself as ventricular late potentials which arelow-amplitude signals that occur after the end of the QRS complex andare thought to reflect slow and fragmented myocardial conduction. (SeeSimson. Am J Cardiol 1983; 51:105-112). Late potentials have beencorrelated with abnormal signals found during electrophysiologicalstudies in segmental sections of the endocardium and represent slowlyactivated tissue that can represent substrate for reentry. (See Simson.Am J Cardiol 1983; 51:105-112).

Signal averaged electrocardiography (SAECG) is a technique wheremultiple QRS complexes are digitized, averaged, filtered, and furtherprocessed with spectral analysis to facilitate late potential analysis.The sensitivity and specificity of an abnormal SAECG for the predictionof SCD or arrhythmic events has been reported to vary from 30% to 76%and the specificity from 63% to 96% (Bailey. J Am Coll Cardiol 2001;38:1902-1911). Conversely, the negative predictive value is high,exceeding 95%, also reflecting the low prevalence of SCD.

There are limited data evaluating the prognostic value of SAECG inpatients with an LVEF greater than 35% and what data exists has beeninconsistent (Ikeda. J AmColl Cardiol).

Microvolt T-Wave Alternans

Microvolt T-wave alternans (MTWA) is a technique developed to identifyinstability of ventricular repolarization during exercise. Thisinstability can lead to dispersion of ventricular refraction, and hasbeen promoted as another methodology for risk stratification for SCD.(See Pastore. Circulation 1999; 99:1385-94; Verrier, R. L., et al.,Microvolt T-wave alternans physiological basis, methods of measurement,and clinical utility—consensus guideline by International Society forHolter and Noninvasive Electrocardiology. J Am Coll Cardiol, 2011.58(13): p. 1309-24).

A large meta-analysis has shown prognostic value of a negative MTWA testin post-myocardial infarction (post-MI) patients with reduced ejectionfraction, with the strength of the test resulting mainly from a veryhigh negative predictive value (See Gehi. J Am Coll Cardiol 2005;46:75-82). Additional studies evaluating the prognostic value of MTWAhave been inconsistent (See Salerno-Uriarte. J Am Coll Cardiol 2007;50:1896-904; J Am Coll Cardiol 2007; 50:1896-904).

In a large Italian study, more than 400 patients were tested for MTWAand followed over 18-24 months revealing a negative predictive value of97%. However, the patients who tested positive for MTWA represented agroup who had concomitantly been diagnosed with either non-ischemiccardiomyopathy, NYHA Class II/III CHF, or a LVEF less than 40%,representing a group of patients already at high risk for SCD and addinglittle benefit to a low or intermediate risk population. The evidencefor usefulness of MTWA in this population is not well-established andhas generally been limited by poor positive predictive values due to lowprevalence (See Ikeda. J AmColl Cardiol 2006; 48:2268-74).

Ambulatory ECG Monitoring

The detection of ventricular arrhythmias (including prematureventricular contractions (PVCs) and non-sustained ventriculartachycardia (NSVT)) using ambulatory ECG monitoring in patients withleft ventricular dysfunction following myocardial infarction isassociated with an increased risk for mortality (See Bigger. Circulation1984; 69:250-8).

However, there is no significant increased value of ambulatory ECGmonitoring for risk-stratification in high-risk patients. (See Bardy. NEngl J Med. 2005 Jan. 20; 352(3):225-37; Moss. N Engl J Med, Vol. 346,No. 12).

Given currently available data, when evaluating the risk of SCD inpatients without severe LV systolic dysfunction, the value of ambulatoryECG testing is inconclusive and the low positive predictive value ofidentifying NSVT in this patient population may limit its clinicalutility. (See Maggioni. Circulation 1993; 87:312-22).

Heart Failure

The clinical syndrome of congestive heart failure (CHF) can contributeto arrhythmogenesis in patients with ventricular dysfunction and canincrease mortality in patients regardless of LVEF.

Patients with NYHA Class I and II symptoms have been shown to have lowoverall death rates. However, 67% of total deaths were due to SCD. Incontrast, among studies with a mean functional Class IV, there was ahigh total mortality, but the fraction of SCD was only 29% as theincidence of progressive pump failure increased. (See Goldberger.Circulation. 2008; 118:000-000). This paradox continues to have majorimplications on the current utility of ICDs in the low and intermediatepopulation.

Heart failure classification is often dynamic in nature depending on themodality at the time, volume status, medications used at the time, andother comorbid conditions that could influence functional status,thereby limiting its utility.

Metabolic Factors

Factors related to ventricular arrhythmias and SCD include serumcatecholamine levels and electrolyte imbalances. Manifestations ofneurohormonal activation, such as hyponatremia and increased plasmanorepinephrine, renin, and natriuretic peptide levels, have been foundto be predictive of mortality as well. (See Pratt. Circulation 1996;93:519-524).

Autonomic Control

Autonomic imbalance has been implicated in SCD, possibly due to reducedvagal tone and sympathetic enhancement, favoring the formation oflife-threatening arrhythmias. Markers of autonomic control, such asheart rate variability (HRV), baroreflex sensitivity (BRS), and heartrate turbulence (HRT), have been found to have independent and in somecases additive prognostic value for SCD. While this effect is moreprominent in patients with a reduced ejection fraction, some trialsshowed significant risk even for patients with relatively preserved EF.(See Lombardi. Cardiovasc Res (2001) 50 (2): 210-217).

Numerous studies have explored the prognostic value of HRV parametersfor predicting outcomes in postinfarction patients and have consistentlyshown depressed HRV is associated with increased mortality. (SeeLombardi. Cardiovasc Res (2001) 50 (2): 210-217). However, dataregarding the prognostic significance of HRV for predicting SCD inpatients with ischemic heart disease is lacking, and serves no role inrisk-stratification. All data to date concerning autonomic control inpatients with a relatively preserved LVEF has not proven significant.(See De Ferrari. J Am Coll Cardiol 2007; 50:2285-90).

Based on the foregoing, at present there are no methods with acceptablesensitivity or specificity to be clinically useful in the identificationof people at risk for SCD. There is a critical need for a device able toidentify individuals at risk for SCD (independent of any underlyingpathology) with high sensitivity and specificity. This is particularlytrue because there are presently available technologies able to preventSCD in individuals at risk (e.g., ICDs). There is also a great need toaccurately identify patients in currently recognized high-risk groupsfor SCD who would not benefit (and possibly suffer) from high-cost ICDplacement.

Point of convention: In the literature, the term Sudden Cardiac Death(SCD) refers to 1) the occurrence of those ventricular arrhythmiaswhich, if not immediately and successfully treated (e.g., by an externalor implantable cardioverter-defibrillator) lead to death or 2) the deathof an individual from such a ventricular arrhythmia. In contrast, someauthors refer to the occurrence of these most often lethal ventriculararrhythmias as Sudden Cardiac Arrest (SCA), and use the term SCDspecifically in those cases in which the arrhythmic event results indeath. Throughout the remainder of this specification, the first andmore commonly used convention is intended.

BRIEF SUMMARY OF THE TECHNOLOGY

The present technology relates to a non-invasive, quantitative riskstratification methodology for determining an individual's risk for SCD.The input data for the methodology described is digitized ECG data.

In accordance with one aspect of the technology, a method forquantitative stratification of sudden cardiac death (SCD) risk usingdigital electrocardiogram (ECG)-type data is obtained from anindividual. The technology may begin with the preprocessing of suchdigital ECG input data. Such preprocessing may include denoising,detrending and normalization. A plurality of mathematical models maythen be constructed corresponding to the data from each input ECG lead.Control model systems may then be constructed to correspond to eachmodel. Using a plurality of techniques, the relativestability/instability of each control model system may be determined.The calculated system stabilities (for all systems corresponding to allECG leads of any given individual) may be combined into an overallstability/instability measure. Finally, the overallstability/instability measure may be converted into a value whichcorresponds to the individual's risk for SCD.

In one embodiment, digital ECG data from an individual may be obtainedfrom a single lead, three lead, or twelve lead ECG sensor or ECGmachine.

In some cases, the method may further include preprocessing, with apreprocessor, such as a processor that detrends (removing baselinewander), denoises (removing noise from electrical, mechanical,respiratory, white noise sources, etc.) and normalizes. Methods used fordenoising may include wavelet packet techniques.

In accordance with an aspect of the technology, an apparatus forquantitative stratification of sudden cardiac death (SCD) risk usingdigital electrocardiogram (ECG)-type data of an individual, may includecircuitry configured to control: preprocessing input digital ECG-typedata taken from the individual to detrend, denoise, and normalize inorder to obtain preprocessed ECG-type data; generating a plurality ofmathematical ECG-derived models corresponding to the preprocessedECG-type data; generating ECG-derived control model systemscorresponding to each ECG-derived model; determining system stabilitiesof the ECG-derived control model systems, by a plurality of techniqueswhich may include analyzing responses of the ECG-derived control modelsystems to a plurality of impulses; and determining, based on thesystems stabilities, a derived SCD risk for each individual's ECG.

In accordance with an aspect of the technology, a non-transitory storagemedium may include a program executable by a computer. The program mayinclude preprocessing digital ECG-type data by detrending, denoising,and normalizing, to obtain preprocessed ECG-type data; generating aplurality of mathematical ECG-derived models corresponding to thepreprocessed ECG-type data; generating ECG-derived control model systemscorresponding to each ECG-derived model; determining system stabilitiesof the ECG-derived control model systems by a plurality of techniques;and determining, based on the system stabilities, a derived SCD risk forthe individual.

A beneficial application of the present technology is a method, such asin a processor or other processing apparatus, for determining anindividual's risk for SCD, such as with high sensitivity and specificity(e.g., greater than about 95%) with p-value <0.001.

BRIEF DESCRIPTION OF THE DRAWINGS

The present technology is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings, in whichlike reference numerals refer to similar elements including:

FIG. 1 is an illustrative overview of an SCD risk determinationapparatus for determining SCD risk from input digital ECG-type dataobtained from an individual in accordance with the present technology.

FIG. 2 shows an example clinical application of the apparatus of FIG. 1in accordance with the present technology.

FIG. 3 illustrates a flow chart of an overall process performed by anSCD risk determination apparatus in accordance with the presenttechnology.

FIG. 4 shows a component diagram of an SCD risk determination apparatusin accordance with the present technology.

FIG. 5 is a block diagram of an example implementation of selectedcomponents of the apparatus of FIG. 4.

FIG. 6 is an exemplary implementation of a SCD risk determinationapparatus in accordance with the present technology.

FIG. 7 is an exemplary implementation of a SCD risk determinationapparatus in accordance with the present technology.

DETAILED DESCRIPTION

Before the present technology is described in further detail, it is tobe understood that the technology is not limited to the particularexamples described herein, which may vary. It is also to be understoodthat the terminology used in this disclosure is for the purpose ofdescribing only the particular examples discussed herein, and is notintended to be limiting.

The following description provides specific details of aspects of thetechnologies detailed herein. The headings and subheadings providedherein are for convenience and ease of reading only.

1. OVERVIEW

The present technology described herein generally relates toquantitatively identifying any individual's risk for Sudden CardiacDeath (SCD), which may also be termed Sudden Cardiac Arrest (SCA), usingnoninvasive methods. Such noninvasive mechanisms may include the designof linear and nonlinear mathematical models using input digitalelectrocardiogram (ECG)-type data obtained from any given individual. Inone embodiment, at least one digital ECG lead may be used for data inputand modeling ECG-derived models and constructing ECG-derived controlmodel systems. The present technology may include determining stabilityof ECG-derived control model systems, which have been generated byincorporating system control operation into the ECG-derived modelsrespectively, in part by analyzing responses of the ECG-derived modelsto perturbations simulating electrical impulses; and determining, basedon the stability determinations, whether the ECG-derived control modelsystems indicate high risk of occurrence of lethal ventriculararrhythmias or SCD for the patient.

Specifically, to design an ECG-derived model, the noninvasive mechanismsdiscussed herein may analyze digital input ECG-type data from anyindividual, such as a patient in a study including several test groupsof patients.

The input digital ECG-type data may, for example, be obtained from astandard, resting 12-lead ECG machine, or, alternatively, from a single,three, or twelve skin-potential sensors in the complete absence of anyECG machine. This data in all cases is obtained in a noninvasive manner.

The ECG-derived mathematical models constructed for quantitative riskdetermination may be designed by the model processor through the use oflinear and nonlinear mathematical modeling techniques, to obtain linearand nonlinear ECG-derived models.

In one embodiment, the ECG-derived models may be constructed for eachpatient in a study. The ECG-derived models are unique for eachpatient/ECG, and may be generated to represent conduction of electricalimpulses in the myocardium.

A single or numerous ECG-derived model(s) are derived using a variety ofSystem Identification (SI) based techniques. A Control Model System isconstructed to correspond to each model. The electrical stability ofeach ECG-derived control model system may then be determined by analysisof the impulse response of each ECG-derived control model system. Timeas well as frequency properties of the impulse, may be used in thiscalculation. In addition, system stability margin methods may be used instability determination. For each individual, stability values may beused to determine the overall risk

By way of illustration, FIG. 1 shows an example operating environment ofthe present technology. The present technology may include a SCD riskdetermination apparatus 120 including a data preprocessor 122, anECG-derived model generator 124, an ECG-derived control model systemgenerator 126, a system stability/instability determinant 128 and an SCDrisk determiner 130. The apparatus may require as input, e.g., digitalECG data, collected from a patient in order to determine SCD risk.

The data preprocessor 122 may process input digital ECG-type data. Suchpreprocessing may include detrending, denoising, and normalization.

The ECG-derived model generator 124 may generate one or moremathematical models in the form of ECG-derived models, which includelinear and nonlinear models, using linear and nonlinear modelingtechniques, from the preprocessed digital ECG-type data.

The ECG-derived control model system generator 126 may incorporatesystem control operation features into the ECG-derived models, togenerate, respectively, ECG-derived control model systems. Thedeterminant 128 may determine stability/instability of the ECG-derivedcontrol system models, by analyzing responses of the ECG-derived controlmodel systems to impulses.

The SCD risk determinator 130 may generate an SCD risk determination forany individual, which is an overall SCD risk value based on results ofthe stability/instability determinations for the ECG-derived controlmodel systems constructed using of the individual's input digitalECG-type data.

A clinical trial can be designed to determine the overall sensitivityand specificity of the technology described herein. Such a clinicalstudy may include 300 patients from which digital ECG-type data iscollected, where the patients may be separated into three patient groupswith 100 per group. The first group of digital ECG-type data may beobtained from 100 patients with no history of heart disease and nohistory of a sudden cardiac arrest (SCA)/sudden cardiac death (SCD)-typeevent. The second group of digital ECG-type data may be obtained from100 patients with a history of heart disease, but no history of SCA/SCD.The third group of digital ECG-type data may be obtained from 100patients with or without a history of heart disease, but with a historyof a SCA or SCD-type event. With this data, the technology discussedherein is used to determine an SCD risk score based upon the relativestability/instability of all models designed corresponding to the inputdigital ECG-type data obtained from each subject of the study. Thesensitivity and specificity of the technology described herein isdetermined by comparing the calculated SCD risk score of each subject tothe subject group to which each belongs.

For instance, the apparatus 120 may be implemented in an individualapparatus, e.g., an ECG device, a general monitoring device, aHolter-type recording device, a PC chip, an ICD, or ATP-ICD(antitachycardia pacing ICD), SCD ablation equipment, etc., or as aself-contained unit.

In some examples, the apparatus 120 may be used once or on multipleoccasions in any given individual or many different individuals. Thus,the apparatus 120 can be a tool in clinical practice. For instance, asillustrated in FIG. 2, the apparatus 120 may operate in concert with astandard ECG device 145 that measures the digital ECG data of anindividual 115 via electrodes 132. The apparatus 120 may determine theindividual's risk for SCD and display on a screen this SCD risk value.

FIG. 3 is a flowchart that illustrates an embodiment of a method ofoperation of the present technology. Methods illustrated in the flowchart of FIG. 3 and other flowcharts discussed herein may be executed byprocessors. In some examples, methods illustrated in each flow chart maybe carried out periodically, continuously, as needed, as triggered, orin another manner. Each method may include one or more operations,functions, or actions as illustrated by one or more of the blocks. Ablock may represent a process of information, a transmission ofinformation, or a combination thereof.

In a flowchart, although the blocks are illustrated in a sequentialorder, these blocks may also function in parallel or in a differentorder than those described herein, depending on the functionalitiesinvolved. Also, the various blocks may be combined into fewer blocks,divided into additional blocks, sub-blocks, or omitted based upon thedesired implementation. Furthermore, blocks illustrated in various flowcharts may be combined with one another, in part or in whole, based onthe functionalities involved.

Referring to FIG. 3, at block 302, the data preprocessor 122 may obtaindigital ECG-type data unique to a patient. The digital ECG-type dataobtained for generating the digital ECG-derived models is not derivedfrom any ECG tracings.

At block 304, as discussed in detail below, the preprocessor 122 mayperform preprocessing on the digital ECG-type data in a manner optimalfor use in SCD risk determination. Such preprocessing may includedetrending, denoising, and normalization using techniques which mayinclude FIR (Finite Impulse Response) as well as wavelet packetfiltering.

At block 306, the generator 124 may generate multiple ECG-derived modelsfor each preprocessed (by block 304) digital ECG-type data sampleobtained for each ECG lead used to measure an ECG of the patient. TheECG-derived models then may be tested by the generator 104 for accuracy,validation and prediction.

At block 308, the generator 126 may, following verification andvalidation of the digital ECG-derived models, modify the digitalECG-derived models to include system control operations, to obtaindigital ECG-derived control model systems corresponding to each digitalECG-derived model. For example, negative feedback loops and PIDs(proportional integral derivative) may be incorporated into each digitalECG-derived control model system, and PID tuning may be completed.

At block 310, the determinant 128 may determine system stability orinstability of each digital ECG-derived control model system for eachdigital ECG data sample for each digital ECG lead of a patient, using avariety of stability/instability determination techniques. For example,the determinant 128 may mathematically apply electrical impulses in theform of perturbations to the digital ECG-derived control model systems,and analyze the results of the perturbations which are outputs of thedigital ECG-derived control model systems, to determine system stabilityor instability of the digital ECG-derived control model systems. In oneembodiment, the system stability analyses may use a variety of controltheory techniques such as BIBO (bounded input-bounded output) methods,Nyquist and Bode plots, Routh-Hurwitz criteria, pole transform functionanalysis, eigenvalue analysis, robust margin stability, and Lyapunovstability methods.

At block 312, the determinator 130 may calculate an overall stabilityvalue for each digital ECG lead of the individual, based on thestability determined for the digital ECG-derived control model systemsrespectively corresponding to the digital ECG leads.

At block 314, the determinator 130 may determine a determined SCD riskfor the individual, based on the overall stability values of theindividual.

In one embodiment, an overall stability is determined based upon allcontrol model systems developed for each digital ECG lead of eachindividual being studied. From these values, the risk for SCD for anygiven individual/digital ECG is determined.

In an embodiment in which the present technology is implemented in astudy of 300 patients grouped into three groups as described above, SCDrisk for each individual/digital ECG in the study is obtained. Thesensitivity and specificity of these SCD risk values may be determinedand verified, by revealing the patient group from which each patient inthe study belongs.

At block 316, the determinator 130 may output the SCD risk for theindividual, for example, for rendering on a display.

2. SCD RISK DETERMINATION APPARATUS 2.1 General Operation of the SCDRisk Determination Apparatus

As described earlier, the processes of the SCD risk determinationapparatus 120 may include generating linear and nonlinear mathematicalmodels for any given individual/ECG based upon the input digitalECG-type data obtained from the individual. This may be performed inseveral steps.

Digital ECG data can be obtained either directly from one or moredigital ECG leads, or from any skin sensor. At a first step, the devicemay use as input digital ECG data. This data may be obtained in oneexample from a standard, resting 12-lead digital ECG machine.

In one embodiment, the digital ECG lead placement is the standard ECGlead placement. In this embodiment, the apparatus can operate upon 12digital ECG signals from an individual.

At a second step, the digital ECG data obtained from the patient, whichmay be stored in the memory of the apparatus, may be preprocessed by apreprocessor of the apparatus. The preprocessor may performpreprocessing in a manner to modify the input data into a format optimalfor SCD risk determination. The preprocessing may include detrending,denoising, and normalization. This is accomplished using techniquesincluding wavelet packet analysis. This extensive preprocessing mayresult in a data form optimal for control model system construction andultimate SCD risk determination, according to the technology describedherein.

Subsequently, all of the fully preprocessed digital ECG-type data maythen be further operated upon in a manner which determines SCD risk in agiven individual.

The generation of the ECG-derived models and ECG-derived control modelsystems may be performed in the apparatus such as in a processing unitthereof. In such a processing unit, the preprocessed digital ECG-typedata is completely operated upon. A single or multiple distinct model(s)are constructed corresponding to the preprocessed digital ECG datacorresponding to each ECG lead of each individual.

In some embodiments, the ECG-derived models may be generated usingnumerous techniques, including, but not limited to system identificationmethods. Linear models may include parametric models, impulse-responsemodels, and frequency-response models, such as state-space, transferfunctions, and spectral models. In addition, models may be generatedbased on AR (Auto Regression), ARMA (Auto Regression Moving Average),and polynomial-based systems. In addition, linear modeling techniquesmay include some or all of the following: state space, time domain andfrequency domain methods. Further, nonlinear models may be constructedusing techniques such as nonlinear auto regression exogenous (ARX), autoregressive integrated exogenous (ARIX), ARIMAX exogenous, transferfunctions, Hammerstein-Wiener methodology and Box-Jenkins (BJ)techniques.

The processing, by using one of more the above methods and modelgeneration techniques, may create mathematical models based upon anindividual's digital ECG-type data. The digital ECG-derived models thusobtained are unique for every lead of every ECG of every individual.

In all cases, the digital ECG-derived models are verified and validated.

2.2 Example Components of Apparatus 120

FIG. 4 is a schematic diagram of an exemplary implementation of theapparatus 120. The apparatus 120 may include one or more of thefollowing components: one or more preprocessors such as preprocessor160, one or more processors 162, a storage device 142, an input device144, and an output device 146. Components of the apparatus 120 may becommunicatively coupled together in either a wired or wireless fashion.In some cases, the methodologies of the processing components may beachieved in a single processor or multiple processors. In one example asillustrated in FIG. 4, the components may be coupled together by asystem bus 148. A detailed description of each component is as follows.

2.2.1 Preprocessor and Processor

The preprocessor 160 and processor 162 may control and execute thefunctions of the blocks of FIG. 3. For instance, the preprocessor 160may perform the following functions, including detrending, denoising,and normalization of the digital input data. The processor 162 mayperform the following operations, including generating a mathematicalmodel based upon preprocessed (by block 304) digital ECG-type data as adigital ECG-derived model using linear and nonlinear modeling methodsand techniques, generating digital ECG-derived control model systems byrespectively modifying the digital ECG-derived models for system controloperation, determining stability of the digital ECG-derived controlmodel systems by methods including analyzing responses thereto toperturbation, and determining SCD risk for the individual/ECG based onthe stability determinations. The preprocessor 160 and processor 162 maybe of any type including but not limited to a general purposepreprocessor or processor and a special purpose or dedicatedpreprocessor or processor, e.g., an application-specific integratedcircuit (ASIC), a digital signal processor (DSP), a graphical processingunit (GPU), a floating point processing unit (FPU), and the like. Thepreprocessor 160 and the processor 162 may refer to a single processor,or a collection of processors of the same type or various types, whichmay or may not operate in a parallel-processing mode.

The preprocessor 160 and the processor 162 may communicate with othercomponents of the apparatus 120. In one example, the preprocessor 160and the processor 162 may execute computer-readable instructions orother instructions stored in the storage device 142. The preprocessor160 and the processor 162 may read and write the data during executionof the computer-readable instructions. In another example, thepreprocessor 160 may act upon input signals provided by the input device144.

2.2.2 Storage Device of Apparatus

The storage device 142 may provide storage data for the apparatus 120 byusing one or more non-transitory computer-readable media. Thecomputer-readable media may store volatile data, non-volatile data, or acombination thereof. Some computer-readable media may store data for ashort period of time. Other computer-readable media may store datapersistently for a long period of time.

The computer-readable media may include primary storage, secondarystorage, or a combination thereof. The primary storage may be simplyreferred to as memory, which is directly accessed by the preprocessor160 and the processor 162. The secondary storage may be indirectlyaccessed by the preprocessor 160 and processor 162 via the primarystorage.

The computer-readable media may be of different types includingrandom-access memory (e.g., SRAM and DRAM), read-only memory (e.g., MaskROM, PROM, EPROM, and EEPROM), non-volatile random-access memory (e.g.flash memory), a magnetic storage medium, an optical disc, a memorycard, a Zip drive, a register memory, a processor cache, a solid statedrive (SSD), and a redundant array of independent disks (RAID), amongother possibilities.

The storage device 142 may store one or more computer-readableinstructions, data, applications, processes, threads of applications,program modules and/or software, which are accessible or executable bythe preprocessor 160 and the processor 162 to perform at least part ofthe herein-described methods and techniques.

By way of example, the computer-readable instructions in the storagedevice 142 may include logic that generates digital ECG-derived modelsand digital ECG-derived control model systems, in one example appliesimpulses to digital ECG-derived control model systems, and analyzesresponses to the impulses for determining SCD risk information.

Examples of data stored in the storage device 142 may include but arenot limited to variables, results and data obtained from one or moredigital ECG devices, the digital ECG-derived models and digitalECG-derived control model systems, and equations, formula and algorithmsused to determine model control system stability/instability.

2.2.3 Input Device

The input device 144 may refer to one or more peripheral devicesconfigured to receive information from individuals. The input device 144may communicate such information to other components of the apparatus120.

By way of example, the input device 144 may be one or more digital ECGleads, a digital ECG device, such as a standard resting 12-lead digitalECG device or device with more or fewer of such electrode leads. Theinput device 144 may also include user input components such as akeyboard, keypad, touch pad, point device, track ball, joystick, voicerecognition device, touch-sensitive surface, microphone, digital camera,mouse, buttons, switch, scroll-wheel, scanner, GPS receiver, movementsensor, location sensor, infrared sensor, optical sensor, RadioFrequency identification (RFID) system, and wireless sensor, amongothers. In some examples, the input device 144 may include an externaldefibrillator or implantable cardioverter-defibrillator (ICD orATP-ICD).

The input device 144 may provide a number of different types of digitalinput data, such as a digital ECG measurement, an electrogram (EGM)measurement, audio data from a microphone, text data from a keypad,video or image data from a camera, and gesture data from a touchpad,just to name a few. This data may be gathered from clinical studies ongroups of individuals such as with other devices and transferred indigital form to the preprocessor 160 via the input.

2.2.4 Output Device

The output device 146 may communicate one or more outputs of thedeterminator 130. The output device 146 may include output componentssuch as a digital output file, a digital output storage device, a visualdisplay, audio transducer, light indicator, tactile transducer, printer,light bulb, and vibration generator, among others. The output device 146may provide a number of different types of output data, such as digitaldata, visual output via a display, audio output via a speaker, andtactile output via a vibration generator, among others.

Also, the output device may be a digital storage device. In someexamples, the output device 146 may include one or more audiotransducers in the following forms: a speaker, headset, jack, earphone,and audio output port.

2.3 Example Logic and Methods

The apparatus 120 may include computer algorithms such ascomputer-readable instructions, ASICs, FPGAs, DSPs, integrated circuits,modules, firmware, or a combination thereof, among other possibilities,to implement the functions of the present technology, for example, asillustrated in flow diagram 300 and executed by the preprocessor 160 andpreprocessor 162 of the apparatus 120. These computer algorithms may beimplemented in a signal bearing non-transitory computer-readable storagemedium in a variety of forms. The apparatus 120 may perform only once orbe reused several times to obtain digital ECG-type data from patients,such as of different clinical studies, and determine SCD risk values byprocessing such ECG-type data.

The preprocessor 160 may perform ECG data noise removal, detrending,baseline drift elimination and denoising.

2.3.1 Preprocessor 160

Referring to FIG. 5, a schematic illustration of an exemplaryconfiguration of the preprocessor 160 of the apparatus 120 is shown. Asdiscussed above, the preprocessor 160 may perform processing for digitalECG-type data detrending, normalization, baseline drift elimination anddenoising. The preprocessor 160 may preprocess digital ECG measurements,such as may be obtained from a clinical study or individual patientmeasurement. The digital ECG measurements may include all of the digitalinformation obtained from all leads of any digital ECG's device or fromthe digital ECG lead itself. As illustrated in FIG. 5, the digital ECGmeasurements of any human subject may be obtained from an ECG device 145and digitizer 147 which are part of the input device 144 of theapparatus 120. The digital ECG device 145 may be a standard resting12-lead digital ECG device or such a measurement device of any othernumber of leads. Alternatively, the digital ECG device 145 may be amanagement system, such as the MUSE Cardiology Information System by GEHealthcare, which stores and manages digital ECG measurements output byone or more digital ECG devices. The digital ECG measurement obtainedfrom an individual may include measured voltages obtained from eachlead. For instance, the digital ECG measurement extracted from astandard resting 12-lead digital ECG device for one individual may havetwelve sequences representing digital 12-lead ECG measurements obtainedfrom the individual. A sequence may represent the voltage measures as afunction of time associated with one of the twelve leads: Lead I, LeadII, Lead III, Lead aVR, Lead aVL, Lead aVF, Lead V1, Lead V2, Lead V3,Lead V4, Lead V5, and Lead V6. For example, each individual in threegroups of patients in a study, as described above, may have twelvesequences. Each digital ECG measurement may include measurements takenfrom a period of time (e.g., approximately 10 seconds, or other suitabletime periods). In an alternative embodiment, the unpreprocessed digitaldata may be obtained from a standard data acquisition (DAQ) device.

As illustrated in FIG. 5, the preprocessor 160 may include one or moreof the following subunits: a noise removal subunit 165, a detrending andbaseline drift eliminator (DTBDE) subunit 167 and a denoiser subunit170.

The noise removal subunit 165 may receive digital ECG measurements, andremove electrical noise and movement artifact noise using modificationof the techniques of ECG data normalization as well as wavelet packettechniques. The DTBDE subunit 167 may receive digital data output fromthe subunit 165, following processing by the subunit 165, and furtherprocess the received data for eliminating baseline drift and denoising.

The denoising subunit 170 may receive digital data output from thesubunit 167, following processing by the subunit 167, and remove signalnoise using a Finite Impulse Response (FIR) digital filter. The signalnoise may include mechanical noise, respiration-related noise and whitenoise. In one embodiment, the data received by the denoiser subunit 170may be filtered by a Fourier filter 172 and then wavelet packetfiltering may then performed by the preprocessor 160 for further signaldenoising. The wavelet filters 174 may use several wavelet families at avariety of decomposition levels to further denoise the signals. Thewavelet filter 174 may employ entropy methods to obtain optimalthresholding in order to obtain ideal denoising. The wavelet filter 174may include implementation of a discrete wave transform. Alternatively,the wavelet filter 174 may include implementation of a continuouswavelet transform. Parameters associated with the continuous wavelettransform may be adjusted either automatically or manually.

2.3.2 Processor

Referring again to FIG. 4, the processor 162 may receive as input thepreprocessed ECG-type data output of the preprocessor 160. In theprocessor 162, linear and nonlinear mathematical models of thepreprocessed ECG-type data may be generated. In addition, the processor162 may implement functions of the generator 126, the determinant 128and the determinator 130 to perform mathematical operations with regardto and using the digital ECG-derived models to determine SCD risk forthe patient.

The processor 162 may function to quantitatively determine the risk inany given individual of the occurrence of SCD, using the digitalECG-derived control model systems of the individual. For example, oncethe digital ECG-derived model systems for the patient are generated, theprocessor 162 may determine a SCD risk of the individual to which thedigital ECG-derived control model systems correspond.

In one embodiment, the processor 162 may function by testing the digitalECG-derived models for accuracy, validation, and prediction. Thesensitivity and specificity of the SCD risk for an individual/digitalECG may be determined by multiple quantitative analyses of the resultsgenerated by applying a variety of methods for determining thestability/instability of the corresponding digital ECG-derived controlmodel systems. The perturbations may include step, transfer and impulseresponse methods.

From such analyses, overall stability/instability values for each of theECG leads for an individual/ECG, based on the stability of the digitalECG-derived control model systems determined for the respective ECGleads is determined.

The individual's risk for SCD may be quantitatively derived from theoverall stability/instability values for each of the digital ECG-derivedcontrol model systems corresponding to the individual determined fromthe results of these analyses. In other words, digital ECG-derivedcontrol model system stability/instability is determined andstability/instability values obtained from this determination arequantitatively transformed into SCD risk values.

In this manner, the sensitivity and specificity of an SCD risk apparatusaccording to the present technology may be determined to be greater than95%.

Based upon the analysis of the results obtained by implementing thepresent technology, the relative risk of the patient in a study forexperiencing SCD may be quantitatively determined.

In one embodiment, the risk values corresponding to each ECG-type datasample may be plotted as a scatter plot against the patient group fromwhich the sample was obtained. From the scatter plot, the sensitivityand specificity as well as corresponding p-value may be calculated forthe present technology of determining SCD risk.

Advantageously, according to the technology of the disclosure, risk ofany individual for SCD may be quantitatively determined withsensitivity/specificity >95% with p-value <0.001.

In some cases, the output of the apparatus 120 may be a number rangingfrom zero to one. In such an example, SCD risk scores correlate with SCDrisk as follows:

SCD RISK SCORE SCD RISK 0.00-0.10 very low risk 0.11-0.40 low risk0.41-0.70 moderate risk 0.71-1.00 high risk

Although the output may be a real number, in some versions an index maybe implemented on a suitable scale for a similar stratification of therisk. Similarly, the output may include a message such as textidentifying the nature of the risk (e.g., very low, low, moderate, highetc.). Other formats for stratification may optionally be implemented.

3. OTHER IMPLEMENTATIONS

The implementations of each of the components in the apparatus 120, suchas shown in FIG. 1, including the processes, parts, units and subunitsthereof described of are merely illustrative, and not meant to belimiting. Each apparatus may include other parts, units, subunits, orvariations thereof. For instance, each of the data preprocessor 122, thegenerators 124 and 126, the determinant 128 and the determinator 130 maybe divided into additional parts, units, or subunits.

According to some aspects of the technology, the apparatus 120, alone orin combination with other subunits, may be a plug-in application to astandard digital ECG device, such as a standard resting 12-lead digitalECG. Moreover, the processes and methods described herein may beperformed in whole or in part by a computer or other processingapparatus that may include integrated chips, a memory and/or othercontrol instruction, data or information storage medium. For example,programmed instructions encompassing such methodologies may be coded onintegrated chips in the memory of the device. Such instructions may alsoor alternatively be loaded as software or firmware using an appropriatedata storage medium. With such an apparatus, the device can determinedigital ECG-derived models from previously measured and received digitalECG data, such as data measured by a discrete measuring device.

In some cases, the apparatus may be part of an ATP-ICD (anti-tachycardiapacing (ATP) ICD). In some cases, an apparatus in accordance withaspects of the present technology may be coupled to a defibrillator,e.g., by wireless communication, so as to receive EGM-type data fortesting purposes. Thus, while a 12-lead digital ECG has previously beendescribed, the apparatus according to aspects of the present technologymay be configured to operate on 3-lead EGM signals or any other numberof leads or electrode measurements.

In one embodiment, the present technology may be implemented completelyindependent of any digital ECG machine, and obtain input from anysingle, three or twelve digital ECG-type skin leads.

In one embodiment, referring to FIG. 6, aspects of the presenttechnology may be implemented in an apparatus 506, which has the same orsimilar functionalities as the apparatus 120 described above. Theapparatus 506 may be communicatively coupled to a data acquisitiondevice 504 (DAQ) external to and independent of the apparatus 506. TheDAQ 504 may be utilized to obtain ECG-type data from an individual 502using one or more ECG leads 503, and provide the ECG-type data, withoutany preprocessing so as to be in unpreprocessed form as unpreprocessedECG-type data as described above, to the apparatus 506, via a wirelessor wired transmission medium 507. The apparatus 506, based on theunpreprocessed ECG-type data, may determine SCD risk according to thepresent technology.

In another embodiment, referring to FIG. 7, aspects of the presenttechnology may be implemented in an apparatus 600, which is in the formof a smartwatch having communication capabilities and includes the sameor similar functionalities of determining SCD risk as the apparatus 120described above. The functions of the present technology to determineand output determined SCD risk may be included in an application (APP)stored in a memory of the apparatus 600 and executed by a processor ofthe apparatus 600. In such embodiment, the apparatus 600 may include oneor more skin-potential sensors (not shown) for obtaining ECG-type datafrom the individual when the individual is wearing the smartwatchapparatus, and then determine a determined SCD risk according to thepresent technology from such data.

In one embodiment, the digital ECG-derived models and control modelsystems may be generated by an apparatus using digital EGM data ratherthan digital ECG data as input. Such an apparatus for determining a SCDrisk value from the digital ECG-derived control model systems could beincorporated into an ATP-ICD device to enable anti-tachycardia pacing(ATP) prior to the onset of SCD (SCA), thereby preventing any occurrenceof ventricular tachycardia are ventricular fibrillation.

In addition, the technology of the disclosure may function in real time,and therefore be used to guide ventricular ablation procedures. Thetechnology of the disclosure may be used to determine at the time of anablation procedure (ventricular ablation performed to lower theincidence of SCD in patients at risk for SCD) whether the patient riskfor SCD has been successfully reduced and the procedure can be ended. Atpresent, electrical inducibility of ventricular tachycardia is themethod used to predict the success of ventricular ablation. Thistechnique has not been demonstrated to be a good for determining SCD.

4. SOME POTENTIAL ADVANTAGES OF THE PRESENT TECHNOLOGY

The present technology for generating mathematical models and controlmodel systems from digital ECG-type data and quantitatively determiningan individual's risk for SCD based on an analysis of thestability/instability of these digital ECG-derived control model systemshas many advantages.

First, the present technology may provide noninvasive riskstratification in individuals with high sensitivity and highspecificity.

Second, the device may identify critical information hidden withincomplex data outputs/collections. It may identify digitalelectrocardiogram data responsible or otherwise associated with theonset of Sudden Cardiac Death (such as those measured within a resting,multi-lead digital ECG).

Third, the present technology may perform risk-stratification inindividuals of all risk levels, including no risk, low risk,intermediate risk and high risk. In particular, the present technologymay identify individuals at risk for SCD that are not detectable byprior known techniques.

Fourth, the present technology may identify individuals at risk for SCDwith high specificity and sensitivity levels not previously achieved.

Fifth, the present technology may do so without use of knownfactors—alone or in combination—presently used in SCDrisk-stratification, including left ventricular ejection fraction(LVEF), signal-averaged electrocardiogram (SAECG), microvolt T-wavealternans (MTWA), ambulatory ECG monitoring, heart failure, metabolicfactors and autonomic control. As such, the present technology obviatesthe shortcomings of these technologies as discussed in the backgroundsection, although in some embodiments the assessment may be combinedwith known methods.

Sixth, by identifying individuals at risk for SCD, the presenttechnology has a transformational impact in the initiation ofappropriate treatment of SCD (e.g., ICD implantation) and thereby maygreatly reduce the incidence of SCD.

Seventh, the present technology poses no risk to any individual, otherthan the insignificant risk of undergoing a standard ECG.

Eighth, the present technology may successfully calculate SCD risk inall individuals, regardless of whether the individuals have experiencedcardiac surgery, and regardless of their clinical history, includinghistory of myocardial infarction, atherosclerotic heart disease,cardiomyopathy, cardiac rhythm, and cardiac condition abnormalities. Forexample, unlike most of the presently available risk-stratificationtechnologies, the technology described herein can be performed and usedto determine SCD risk in individuals with common cardiac rhythmdisorders, including atrial fibrillation, premature ventricularcontractions (PVCs), as well as bundle branch blocks and complete heartblock.

Ninth, the present technology may be used in individuals with no knownrisk factors for SCD. This includes athletes at the middle school, highschool, college and professional level, relatives of individuals whohave experienced SCD as well as part of any individuals undergoing aroutine physical exam.

Tenth, the present technology may be used to follow the progression ofSCD risk in any individual.

Finally, the general principles used in the technology described hereinmay be used to extract important parameters and information from a vastvariety of signals, including speech, sound, graphic, visual,mechanical, and electrical devices.

5. CONCLUSION

The present technology may also be configured as below.

(1) A method for quantitative determination of Sudden Cardiac Death(SCD) risk using digital electrocardiogram (ECG)-type data of anindividual, the method including steps of:(a) preprocessing digital ECG-type data by detrending, denoising, andnormalizing, to obtain preprocessed ECG-type data; (b) generating aplurality of mathematical digital ECG-derived models corresponding tothe preprocessed digital ECG-type data; (c) generating digitalECG-derived control model systems corresponding to each digitalECG-derived model; (d) determining system stabilities of the digitalECG-derived control model systems by a plurality of techniques; and (e)determining, based on the system stabilities, a derived SCD risk for theindividual.(2) The method according to (1), further including:determining the SCD risk for the individual with sensitivity andspecificity >95% and p-value <0.001.(3) The method according to (1) or (2),wherein the preprocessing of the digital ECG-type data includes:removing movement and electrical noise from the digital ECG-type data,to obtain first preprocessed data; detrending and eliminating baselinedrift from the first preprocessed data, to obtain second preprocesseddata; and denoising the second preprocessed data.(4) The method according to any one of (1) to (3),wherein the denoising is by at least one of a Finite Impulse Response(FIR) filter or a wavelet denoising method employing an entropycalculation to optimize a threshold setting.(5) The method according to any one of (1) to (4),wherein the denoising is of at least one of mechanical noise,respiration artifacts or white noise.(6) The method according to any one of (1) to (5),wherein the digital ECG-type data for the individual is obtained using astandard resting digital 12-lead ECG, or a single lead or three leadskin sensor input independent of any digital ECG device in concert withor without an external data acquisition (DAQ) device.(7) The method according to any one of (1) to (6),wherein the digital ECG-derived models are generated using linear andnonlinear modeling methods, and wherein the digital ECG-derived modelsare modified for system control operation to obtain the digitalECG-derived control model systems.(8) The method according to any one of (1) to (7),wherein the determining of the system stabilities includes analyzingresponses of the digital ECG-derived control model systems to impulses.(9) The method according to any one of (1) to (8),wherein the digital ECG-derived control model systems include negativefeedback loops.(10) An apparatus for quantitative determination of sudden cardiac death(SCD) risk using digital electrocardiogram (ECG)-type data of anindividual, the apparatus including:circuitry configured to control: preprocessing digital ECG-type data bydetrending, denoising and normalizing, to obtain preprocessed ECG-typedata; generating a plurality of mathematical digital ECG-derived modelscorresponding to the preprocessed digital ECG-type data; generatingdigital ECG-derived control model systems corresponding to each digitalECG-derived model; determining system stabilities of the digitalECG-derived control model systems, by a plurality of techniques; anddetermining, based on the system stabilities, a derived SCD risk for theindividual.(11) The apparatus according to (10),wherein the circuitry is configured to control determining the SCD riskfor the individual with sensitivity and specificity >95% and p-value<0.001.(12) The apparatus according to (10) or (11),wherein the preprocessing of the digital ECG-type data includes:removing movement and electrical noise from the digital ECG-type data,to obtain first preprocessed data; detrending and eliminating baselinedrift from the first preprocessed data, to obtain second preprocesseddata; and denoising the second preprocessed data.(13) The apparatus according to any one of (10) to (12),wherein the denoising is by at least one of a Finite Impulse Response(FIR) filter or a wavelet denoising method employing an entropycalculation to optimize a threshold setting.(14) The apparatus according to any one of (10) to (13),wherein the denoising is of at least one of mechanical noise,respiration artifacts or white noise.(15) The apparatus according to any one of (10) to (14),wherein the digital ECG-type data for the individual is obtained using astandard resting digital 12-lead ECG, or a single lead or three leadskin sensor input independent of any digital ECG device in concert withor without an external data acquisition (DAQ) device.(16) The apparatus according to any one of (10) to (15),wherein the digital ECG-derived models are generated using linear andnonlinear modeling methods, and wherein the digital ECG-derived modelsare modified for system control operation to obtain the digitalECG-derived control model systems.(17) The apparatus according to any one of (10) to (16),wherein the determining of the system stabilities includes analyzingresponses of the digital ECG-derived control model systems to impulses.(18) The apparatus according to any one of (10) to (17),wherein the digital ECG-derived control model systems include negativefeedback loops.(19) A non-transitory storage medium on which is recorded a programexecutable by a computer, the program including:preprocessing digital ECG-type data by detrending, denoising, andnormalizing, to obtain preprocessed digital ECG-type data; generating aplurality of mathematical digital ECG-derived models corresponding tothe preprocessed digital ECG-type data; generating digital ECG-derivedcontrol model systems corresponding to each digital ECG-derived model;determining system stabilities of the digital ECG-derived control modelsystems by a plurality of techniques; and determining, based on thesystem stabilities, a derived SCD risk for the individual.(20) The medium according to (19),wherein the program further includes: generating the digital ECG-derivedmodels using linear and nonlinear modeling methods, and modifying thedigital ECG-derived models for system control operation to obtain thedigital ECG-derived control model systems.

Although aspects of the disclosure herein have been described withreference to particular embodiments, it is to be understood that theseembodiments are merely illustrative of the principles and applicationsof the present disclosure. It is therefore to be understood thatnumerous modifications may be made to the illustrative embodiments andthat other arrangements may be devised without departing from the spiritand scope of the present disclosure as defined by the appended claims.

1. A method of one or more processors for quantitative determination ofsudden cardiac death (SCD) risk of an individual, the method comprising:(a) accessing preprocessed electrocardiogram (ECG)-type data; (b)generating mathematical digital ECG-derived models corresponding to thepreprocessed ECG-type data; (c) generating digital ECG-derived systemcontrol models corresponding to each mathematical digital ECG-derivedmodel; (d) applying perturbations to the digital ECG-derived systemcontrol models; (e) analyzing responses of the digital ECG-derivedsystem control models to the applied perturbations, the analyzingcomprising: calculating stability values based upon the responses of thedigital ECG-derived system control models to the applied perturbations,and determining SCD risk for the individual based upon the calculatedstability values; and (f) outputting the derived SCD risk for display.2. The method of claim 1, further comprising: determining the SCD riskfor the individual with sensitivity and specificity >95% and p-value<0.001.
 3. The method of claim 1, further comprising preprocessingdigital ECG data to generate the preprocessed ECG-type data, thepreprocessing comprising removing noise from the digital ECG data toobtain first-level preprocessed data.
 4. The method of claim 3 whereinthe preprocessing further comprises removing drift from the first-levelpreprocessed data.
 5. The method of claim 1, further comprisingobtaining digital ECG data for the individual from 12-lead, three leador 1 lead devices.
 6. The method of claim 1, wherein the mathematicaldigital ECG-derived models are derived using linear and nonlinear systemidentification techniques, and the mathematical digital ECG-derivedmodels are modified for system control operation.
 7. The method of claim1, wherein the digital ECG-derived system control models includenegative feedback control loops.
 8. A system for quantitativedetermination of sudden cardiac death (SCD) risk of an individual, thesystem comprising: one or more processors configured to: (a) accesspreprocessed electrocardiogram (ECG)-type data; (b) generatemathematical digital ECG-derived models corresponding to thepreprocessed ECG-type data; (b) generate digital ECG-derived systemcontrol models corresponding to each mathematical digital ECG-derivedmodel; (c) apply perturbations to the digital ECG-derived system controlmodels, and (d) analyze responses of the digital ECG-derived systemcontrol models to the applied perturbations, by: calculating stabilityvalues based upon the responses of the digital ECG-derived systemcontrol models to the applied perturbations; determining SCD risk forthe individual based upon the calculated stability values; and (f)output the derived SCD risk for display.
 9. The system of claim 8,wherein the one or more processors is configured to determine the SCDrisk for the individual with sensitivity and specificity >95% andp-value <0.001.
 10. The system of claim 8, wherein the one or moreprocessors is further configured to preprocess digital ECG data togenerate the preprocessed ECG-type data, to so preprocess the one ormore processors is configured to remove noise from the digital ECG datato obtain first-level preprocessed data.
 11. The system of claim 10wherein the one or more processors is further configured to preprocessby removing drift from the first-level preprocessed data.
 12. The systemof claim 8, wherein the system further comprises apparatus configured toobtain digital ECG data for the individual from 12-lead, three lead or 1lead devices.
 13. The system of claim 8, wherein the one or moreprocessors are configured to derive the mathematical digital ECG-derivedmodels using linear and nonlinear system identification techniques, andto modify the mathematical digital ECG-derived models for system controloperation.
 14. The system of claim 8, wherein the digital ECG-derivedsystem control models include negative feedback control loops.
 15. Anon-transitory storage medium on which instructions are stored, theinstructions when executed by multiple processors, cause the multipleprocessors to perform a method comprising: (a) accessing preprocessedelectrocardiogram (ECG)-type data; (b) generating mathematical digitalECG-derived models corresponding to the preprocessed ECG-type data; (c)generating digital ECG-derived system control models corresponding toeach mathematical digital ECG-derived model; (d) applying perturbationsto the digital ECG-derived system control models; (e) analyzingresponses of the digital ECG-derived system control models to theapplied perturbations, the analyzing comprising: calculating stabilityvalues based upon the responses of the digital ECG-derived systemcontrol models to the applied perturbations, and determining SCD riskfor an individual based upon the calculated stability values; and (f)outputting the derived SCD risk for display.
 16. The medium of claim 15,wherein the method further comprises: generating the mathematicaldigital ECG-derived models using linear and nonlinear systemidentification methods; and modifying the mathematical digitalECG-derived models for system control operation.
 17. The medium of claim16, wherein the digital ECG-derived system control models includenegative feedback control loops.